1. Field
The present invention relates to a method of locating features of an object. In particular, but not exclusively, the present invention relates to a method of fitting a model (also referred to as an appearance model) of a class of objects to a target image containing an object within that class.
2. General Background
Statistical models of appearance are widely used in computer vision and have many applications, including interpreting medical images and interpreting images containing faces. For many applications it can be useful to locate object features. For instance, for applications involving images containing faces, it can be useful to locate facial features such as the corners of the eyes and the corners of the mouth.
Conventionally, a statistical model of a class of objects is built representing intensity (greyscale or colour) and/or shape variation across an image or part of an image containing an object of that class. In the case of a facial appearance model, images of faces are used to train the system and thereby generate a model, these images being known as training images. Variation for any given face will tend to include similar patterns, and the model represents these patterns. Once the model has been built, the model may be fitted to target images to identify the locations of features of objects of that class within the target image.
There are several known methods of generating statistical appearance models, and using the models to identify and/or recognise facial features or features of other types of objects in images. A first known model is the Active Shape Model (ASM) described in T. F. Cootes, A. Hill, C. J. Taylor, and J. Haslam: “The use of active shape models for locating structures in medical images”, Image and Vision Computing, 12(6):276-285, July 1994, and in T. F. Cootes, C. J. Taylor, D. Cooper, and J. Graham: “Active shape models—their training and application”, Computer Vision and Image Understanding, 61 (1):38-59, January 1995. Both descriptions of the ASM are herein incorporated by reference.
The basic idea used by the ASM is to establish, from a training set, a pattern of “legal” variation of shapes and spatial relationships of structures in a given class of images (the class of images may be, for, example face images or hand images). Statistical analysis is used to give an efficient parameterisation of the pattern of legal variation, providing a compact representation of shape. The statistical analysis also provides shape constraints, which are used to determine whether the shape of a structure in an analysed image is a plausible example of the object class of interest.
A second known model is the Active Appearance Model (AAM) described in T. F. Cootes, G. J. Edwards, and C. J. Taylor: “Active appearance models”, In H. Burkhardt and B. Neumann, editors, 5th European Conference in Computer Vision, volume 2, pages 484-498. Springer, Berlin, 1998. The AAM uses a combined statistical model of shape and texture. Both the ASM and the AAM were developed at the Victoria University of Manchester, United Kingdom. Both of these models are based upon the use of normalised intensity values. The ASM and the AAM are both generalisations of Eigen-face models. Eigen-face models are based upon the use of intensity values.